Abstract
Digital stethoscopes, combined with artificial intelligence (AI), are transforming clinical auscultation across pediatrics, cardiology, and pulmonology. This review summarizes recent evidence-based studies evaluating AI-assisted digital stethoscopes for the detection and classification of heart and lung sounds. Data indicate improved diagnostic accuracy, enhanced objectivity, and potential applications in low-resource and telemedicine settings. Meta-analysis of key studies shows significant increases in sensitivity, specificity, and AUC compared with traditional auscultation. Future perspectives include wearable devices, multimodal diagnostics, personalized sound profiling, augmented reality integration, and interoperability within the Internet of Medical Things (IoMT).
